Supplementary Materials

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Marine traffic

Table S1. Summary of 2019 marine traffic within the lower Kitimat Fjord System, as reported by archival AIS data.

			Speed (kn)			Length (m)				Beam (m)				Draft (m)
Type	VIDs	Transits	mean	sd	max	mean	sd	min	max	mean	sd	min	max	mean	sd	min	max
Cargo ship	54	157	12.7	1.7	17.1	159	53	26	200	26	8	7	33	8.2	2.6	3.0	11.0
Cargo ship:DG,HS,MP(X)	1	2	11.9	0.7	13.7	67	0	67	67	13	0	13	13	5.0	0.0	5.0	5.0
Diving op.	1	4	7.8	0.7	10.0	10	0	10	10	4	0	4	4	0.5	0.0	0.5	0.5
Dredging or underwater op.	3	36	9.7	2.4	15.0	63	34	35	119	14	6	9	22	4.4	0.5	4.0	5.0
Fishing	308	829	8.4	3.2	32.2	20	9	6	100	6	2	1	20	1.5	1.2	0.4	10.0
HSC	3	8	13.0	8.1	28.6	25	13	7	35	7	3	3	10	2.6	1.7	0.4	4.0
Law enforcement	3	3	9.7	1.5	12.6	42	16	26	68	8	3	6	14	2.8	0.8	2.0	4.0
Local ship	2	10	12.4	6.5	26.5	13	4	10	18	4	0	4	5	0.6	0.2	0.5	0.9
Military op.	1	4	9.1	2.4	15.0	33	0	33	33	8	0	8	8	3.0	0.0	3.0	3.0
Other	22	78	8.6	2.4	24.9	37	22	6	76	9	5	2	20	3.3	1.9	0.3	6.0
Passenger ship	58	803	15.1	5.7	35.6	108	75	11	301	18	10	3	36	4.0	2.2	0.6	8.0
Passenger ship:DG,HS,MP(Y)	1	2	11.1	0.4	11.8	64	0	64	64	12	0	12	12	3.0	0.0	3.0	3.0
Pilot	2	6	19.9	3.1	25.8	19	1	17	20	4	1	3	5	1.0	0.0	1.0	1.0
Pleasure Craft	275	1213	8.2	3.7	37.5	20	16	7	154	6	3	1	18	1.1	1.0	0.4	9.0
Port tender	1	2	7.4	1.1	8.8	26	0	26	26	8	0	8	8	8.0	0.0	8.0	8.0
Sailing	117	426	6.0	1.3	12.6	14	4	8	35	4	1	1	8	0.7	0.3	0.4	3.0
Search/rescue	14	193	10.3	4.0	39.2	52	15	7	83	11	3	2	17	4.6	1.0	0.4	6.0
Tanker	2	7	12.1	0.8	13.9	141	5	134	145	24	0	24	24	8.7	0.5	8.0	9.0
Towing	36	356	7.0	1.7	21.7	27	21	6	162	8	3	4	24	3.3	1.8	0.3	7.0
Towing(200/25)	42	382	8.8	1.6	13.7	32	6	12	41	10	2	2	12	4.7	1.4	1.7	7.0
Tug	62	873	7.4	1.8	13.9	27	28	11	178	8	3	4	22	3.1	1.8	0.6	6.0

 

 

Table S2. AIS traffic in 2019, restricted to prime fin whale habitat in Squally Channel, including Lewis Passage and north Campania Sound (W 129.4519 - 129.26239, N 53.069 - 53.3218)

				Speed (kn)			Length (m)				Beam (m)				Draft (m)
Vessel type	IDs	Transits	Transits/day	mean	sd	max	mean	sd	min	max	mean	sd	min	max	mean	sd	min	max
Cargo > 180m	4	4	0.01	12.4	1.6	14.6	187	8	180	200	31	1	30	32	10.2	0.9	9.0	11
Fishing < 60m	30	75	0.21	8.2	4.3	32.2	17	6	10	37	6	2	4	12	1.2	0.9	0.5	6
Other < 40m	6	20	0.05	8.8	4.9	31.5	26	8	7	40	6	2	2	9	2.0	1.7	0.4	5
Other > 100m	5	9	0.02	10.4	2.0	13.5	157	19	134	178	18	8	4	29	6.6	1.6	4.0	9
Other > 40m	13	59	0.16	9.5	2.4	15.0	55	14	42	98	13	3	9	22	3.9	1.4	2.0	6
Passenger > 180m	5	19	0.05	18.1	2.5	22.0	270	39	197	301	33	1	28	34	7.8	0.4	7.0	8
Pleasurecraft < 40m	70	142	0.39	7.4	3.6	34.4	15	5	7	28	5	1	2	7	0.7	0.2	0.4	2
Sailing	17	35	0.10	5.6	1.4	9.0	15	5	8	32	4	2	2	8	0.8	0.4	0.4	3
Towing < 50m	14	40	0.11	9.2	1.4	11.8	34	4	26	41	10	2	6	12	5.2	1.2	1.7	6
Tug < 50m	17	74	0.20	7.1	1.9	11.2	23	9	12	41	8	2	4	12	3.2	1.7	0.6	6

 

Figure S1. Length distributions of the ten vessel classes used to summarize marine traffic in 2019.

 

Figure S2. Speed distributions, in knots, of the ten vessel classes used to summarize marine traffic in 2019.

 

Figure S3. Seasonal patterns in the speed (knots) of marine traffic, grouped iinto the 10 vessel classes used in this study.

 

Figure S4.Seasonal patterns in the length (meters) of marine traffic, grouped iinto the 10 vessel classes used in this study.

 

Table S3. Trends (historical and predicted) in AIS traffic, 2014 - 2030. Kilometers transited in 2014 - 2019 are observations, while the 2030 numbers are predicted based on linear models. The “scale factor” is the multiplier used, based on those models, to estimate 2030 transits based on 2019 data. “Prop. change” is the proportional rate of change in total transits. The p-value refers to the linear model for each vessel class.

	Kilometers transited						Prop. change
Vessel class	2014	2015	2018	2019	2030	Scale factor	2019	2039	p-value
Cargo > 180m	0	0	0	9444	16943	1.79	0.15	0.08	0.30
Fishing < 60m	17165	16377	31949	45784	86437	1.89	0.12	0.06	0.05
Other < 40m	62201	87269	57399	18723	0	0.00	-0.48	-0.24	0.24
Other > 100m	25213	19011	27281	26166	33552	1.28	0.03	0.03	0.44
Other > 40m	27391	9943	23408	29184	37725	1.29	0.05	0.04	0.60
Passenger > 180m	6212	5489	10403	6168	11554	1.87	0.07	0.04	0.55
Pleasurecraft < 40m	0	0	56	51215	91952	1.80	0.15	0.08	0.30
Sailing	357	1553	12538	20934	50795	2.43	0.19	0.08	0.02
Towing < 50m	35311	15299	39409	42838	67175	1.57	0.08	0.05	0.38
Tug < 50m	30971	44685	46344	43301	61901	1.43	0.05	0.03	0.33

 

Figure S5. Changes in total transit kilometers for the 10 vessel classes in our study, 2014 - 2019.

 

Table S4. Dimensions of the LNG Canada fleet, adapted from TERMPOL (2015). Note that in our analyses, we reduced the max Shell length to 298m, and the beam was adjusted according to the original length:beam ratio.

 

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Methods details

Line-transect surveys

Table S5. Effort and sighting details from line-trasect surveys within the Kitimat Fjord Surveys, 2013 - 2015. For each species, “total” counts include all detections while on systematic effort, while “valid” counts include detections with valid detection-distance estimates.

	Effort		Fin whales		Humpback whales
Year	segments	km	total	valid	total	valid
2013	143	712.5204	8	6	68	38
2014	168	800.5337	18	17	134	130
2015	402	2083.1364	19	19	253	251

 

Figure S6. (a) Design-based line-transect survey effort throughout the central Gitga’at waters of the Kitimat Fjord System (each dot is the center of a 5-km segment of systematic effort), yielding detections of (b) fin whales and (c) humpback whales. Detection dot size reflects group size

 

Table S6. Best-fitting models of the detection functions for fin whales and humpback whales, based upon 2013-2015 line-transect surveys.

	Species	Model	Key function	Formula	C-vM p-value	\(\hat{P_a}\)	se(\(\hat{P_a}\))	\(\Delta\)AIC
1	Fin whale	1	Half-normal	~1	0.8405095	0.5500191	0.0742873	0.0000000
3		2	Half-normal	~1 + factor(year)	0.7815848	0.5231299	0.0742940	0.3047398
2		3	Half-normal	~1 + bft	0.8250065	0.5465154	0.0793721	1.5958236
31	Humpback whale	1	Half-normal	~1 + factor(year)	0.9652267	0.5204825	0.0213641	0.0000000
21		2	Half-normal	~1 + bft	0.9637787	0.5296131	0.0215150	9.0061244
11		3	Half-normal	~1	0.9430440	0.5364511	0.0213979	15.9211900

 

Figure S7. Best-fitting detection function models superimposed upon histograms of detection distances for each species.

 

Figure S8. Bathymetric characteristics of the study area, as summarized for the square-kilometer grid used in density surface modeling.

 

Humpback whale UAS analysis

Table S7. Humpback whale morphometrics (n=13 individuals) measured by UAS in Gitga’at waters in 2019.

	Measurements			Body length (m)				Fluke width (m)
Whale ID	Total	Score 1	Score 2	Mean	SD	Score 1	Score 2	Mean	SD	Score 1	Score 2
20190611 1A	5	3	2	11.19	0.27	11.09	11.34	3.52	0.11	3.49	3.60
20190829 1A	1	1	0	11.08		11.08		3.58		3.58
20190829 1B	4	1	3	10.92	0.33	11.10	10.86	3.76	0.15	3.92	3.69
20190905 1A	10	4	6	11.17	0.62	11.08	11.22	3.93	0.22	3.89	3.96
20190905 1B	2	1	1	11.39	0.20	11.25	11.53	4.21	0.08	4.27	4.15
20190905 2A	4	2	2	14.45	0.29	14.68	14.22	4.38	0.11	4.44	4.33
20190905 2B	3	2	1	13.26	0.28	13.42	12.94	4.21	0.10	4.17	4.28
20190907 1A	3	1	2	11.67	0.22	11.90	11.55	3.98	0.03	3.96	3.99
20190908 1A	1	1	0	10.97		10.97		3.53		3.53
20190909 1A	3	2	1	13.34	0.11	13.34	13.32	4.44	0.12	4.51	4.30
20190909 1B	5	2	3	10.41	0.35	10.77	10.17	3.59	0.10	3.67	3.53
20190910 1A	1	0	1	12.36			12.36	4.01			4.01
20190911 1A	8	3	5	10.94	0.48	10.93	10.95	3.54	0.10	3.54	3.54
20190911 1B	1	0	1	11.08			11.08	3.96			3.96
20191004 1A	9	7	2	13.64	0.21	13.68	13.49	4.13	0.06	4.13	4.09

Table S8. Summary of UAS flights used to estimate humpback whale swimming speed. Multiple follows of the same group were averaged together before calculating speed.

			Duration (sec)		Distance (km)
Date	File	Follows	Mean	Total	Mean	Total	Speed (m/s)
2019-06-06	20190606_DJI_0079	1	31.00	31	0.03	0.03	0.836
2019-08-28	20190828_DJI_0552_Group1	1	43.00	43	0.00	0.00	0.039
2019-08-29	20190829_DJI_0556_Group1	3	50.67	152	0.03	0.08	0.501
2019-09-03	20190903_DJI_0564_Group1	1	68.00	68	0.07	0.07	1.017
2019-09-05	20190905_DJI_0575_Group1	1	71.00	71	0.01	0.01	0.210
2019-09-05	20190905_DJI_0578_Group1	2	84.50	169	0.04	0.07	0.430
2019-09-05	20190905_DJI_0582_Group2	1	42.00	42	0.03	0.03	0.768
2019-09-05	20190905_DJI_0585_Group2	2	50.50	101	0.02	0.04	0.353
2019-09-07	20190907_DJI_0591_Group3	2	48.00	96	0.03	0.06	0.675
2019-09-07	20190907_DJI_0593_Group3	2	98.00	196	0.07	0.15	0.754
2019-09-08	20190908_DJI_0597_Group1and2	1	41.00	41	0.02	0.02	0.580
2019-09-08	20190908_DJI_0600_Group3	1	64.00	64	0.03	0.03	0.525
2019-09-11	20190911_DJI_0620_Group2	2	46.50	93	0.04	0.08	0.812
2019-10-04	20191004_DJI_0624_Group1	1	78.00	78	0.05	0.05	0.663

Fin whale dive tag analysis

Figure S9. Raw time- and depth-distributions of depth sensor readings for each of the 7 SPLASH-10 tag deployments.

 

Figure S10. Time distribution (hour of day, color-coded by daytime/nighttime) of depth samples from SPLASH10 tags, displayed for each deployment separately.

 

Table S9. Summary of SPLASH10 depth data used in fin whale depth distribution analysis.

Deployment ID		Deployment period			Depth sample size
This study	Nichol et al. (2018)	Start	Stop	Hours	Hours valid	Total	Day	Night	Prop. valid
1	132219-132219	2013-08-19 23:00:00	2013-08-24 20:58:45	117.97917	8.00000	384	336	48	0.07
2	132220-132220	2013-08-18 18:47:30	2013-08-28 19:57:30	241.16667	26.18750	1257	837	420	0.11
3	137684-137684	2014-08-16 00:15:00	2014-08-16 23:57:30	23.70833	23.72917	1139	835	304	1.00
4	137685-137685	2014-08-20 18:22:30	2014-09-04 21:58:45	363.60417	45.62500	2190	1720	470	0.13
5	137686-137686	2014-08-23 16:30:00	2014-09-02 13:57:30	237.45833	18.47917	887	792	95	0.08
6	142546-142546	2014-09-08 19:00:00	2014-09-28 23:58:45	484.97917	82.00000	3936	2468	1468	0.17
7	142547-142547	2014-09-14 00:00:00	2014-09-14 10:58:45	10.97917	4.00000	192	96	96	0.36

 

Figure S11. Daytime (left) and nighttime (right) depth distribution curves, representing the proportion of time spent above a given depth, for six SPLASH-10 deployments on fin whales (colored lines).

 

Collision & mortality analysis

 

Figure S12. Probabilities of collision (left) and mortality (right) as a function of ship speed (>180m length), adapted from Gende et al. (2011) and Kelley et al. (2020), respectively.

 

Potential Biological Removal

Fin whales – Canadian Pacific stock (Wright et al. 2022):

pbr(N = 2893, CV = 0.15) %>% cbind

##         .       
## PBR     16.1527 
## Nmin    807.6348
## Rmax    0.08    
## Fr      0.5     
## Nmedian 2876.57

Fin whales – North Coast Sector (Wright et al. 2022):

pbr(N = 161, CV = 0.50)  %>% cbind

##         .       
## PBR     1.264424
## Nmin    63.2212 
## Rmax    0.08    
## Fr      0.5     
## Nmedian 165.0474

Fin whales – coastal (Queen Charlotte, Hecate Strait) (Nichol et al 2017):

pbr(N = 405, CV = 0.6)  %>% cbind

##         .       
## PBR     2.843767
## Nmin    142.1884
## Rmax    0.08    
## Fr      0.5     
## Nmedian 411.2598

Humpback whales – Canadian Pacific stock (Wright et al. 2022):

pbr(N = 7030, CV = 0.1)  %>% cbind

##         .       
## PBR     35.1167 
## Nmin    1755.835
## Rmax    0.08    
## Fr      0.5     
## Nmedian 6937.317

Humpback whales – North Coast sector (Wright et al. 2022):

pbr(N = 1816, CV = 0.13)  %>% cbind

##         .       
## PBR     10.15118
## Nmin    507.5589
## Rmax    0.08    
## Fr      0.5     
## Nmedian 1867.264

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Results details

Vessel traffic

Figure S13. Distribution of 2019 marine traffic parsed by waterway and time of day.

 

Figure S14. Monthly distribution of 2019 marine traffic, parsed by time of day.

 

Figure S15. Transit counts for 10 vessel types in 2019, displayed for each waterway in the study area separately.

 

Species distribution models

Table S10. Best-fitting density surface models for fin whales and humpback whales for mid-June – early-September.

Species	Formula	Trunc. dist.	Family	Link function	Delta AIC	Deviance explained
Fin whale	(Lat x Lon) + seafloor depth + seafloor range	2.0 km	Tweedie	log	104	54%
Humpback whale	(Lat x Lon x DOY) + seafloor depth + seafloor range + year	2.7 km	Tweedie	log	14	51%

 

Table S11. Fin whale density (bootstrapped 95% confidence interval) by waterway (whales per square km), , as estimated from the best-fitting density surface model.

Waterway	Season
Caamano	0.022 (0-0.126)
Campania	0.024 (0-0.148)
Estevan	0 (0-0)
McKay	0 (0-0)
Squally	0.031 (0-0.169)
Verney	0 (0-0)
Whale	0 (0-0)
Wright	0 (0-0)
Study area	0.014 (0-0.118)

Table S12. Humpback whale density (bootstrapped 95% confidence interval) by waterway (whales per square km), , as estimated from the best-fitting density surface model.

Waterway	Season	June	July	August	September
Caamano	0.059 (0.012-0.153)	0.119 (0.006-0.74)	0.057 (0.006-0.113)	0.046 (0.005-0.094)	0.049 (0.013-0.139)
Campania	0.07 (0.012-0.139)	0.056 (0.001-0.131)	0.063 (0.01-0.158)	0.071 (0.007-0.186)	0.097 (0.015-0.249)
Estevan	0.037 (0.004-0.071)	0.119 (0.006-0.153)	0.021 (0.001-0.024)	0.046 (0.006-0.121)	0.047 (0.008-0.107)
McKay	0.049 (0.003-0.112)	0.007 (0.001-0.036)	0.02 (0.001-0.04)	0.068 (0-0.134)	0.102 (0.017-0.313)
Squally	0.11 (0.025-0.251)	0.132 (0.021-0.429)	0.041 (0.002-0.099)	0.161 (0.037-0.448)	0.102 (0.01-0.2)
Verney	0.072 (0.006-0.282)	0.006 (0-0.035)	0.029 (0.001-0.108)	0.085 (0.006-0.231)	0.154 (0.004-0.786)
Whale	0.113 (0.023-0.298)	0.03 (0.003-0.111)	0.015 (0.002-0.048)	0.196 (0.026-0.502)	0.165 (0.034-0.512)
Wright	0.117 (0.007-0.308)	0.033 (0-0.139)	0.081 (0.01-0.204)	0.164 (0.034-0.566)	0.154 (0-0.425)
Study area	0.079 (0.01-0.223)	0.083 (0.001-0.417)	0.046 (0.002-0.125)	0.1 (0.007-0.359)	0.095 (0.01-0.32)

 

Seasonality

Table S13. Summary of GAM of seasonal fin whale abundance. This model was used to scale the June-September density estimate.

Family	Formula	edf	P-value of coefficient	Deviance explained
Negative binomial	count ~ s(doy, k=5) + offset(log(minutes))	2.803	7e-04	26%

 

Close-encounter rates

 

Table S14. Close encounter rate estimates for each species and vessel class used in this study. Here summertime and wintertime distributions are pooled.

	Fin whales						Humpback whales
Vessel type	Median	Mean	SD	LCI	UCI		Median	Mean	SD	LCI	UCI	FW - HW
Cargo > 180m	0.06	0.061	0.024	0.02	0.11		0.05	0.048	0.022	0.01	0.10	0.01
Cedar LNG tanker in-heel	0.08	0.086	0.030	0.04	0.15		0.07	0.070	0.025	0.03	0.12	0.01
Cedar LNG tanker in-product	0.08	0.086	0.031	0.03	0.14		0.07	0.067	0.025	0.03	0.12	0.01
Cedar LNG tug in-heel	0.02	0.024	0.015	0.00	0.06		0.01	0.016	0.013	0.00	0.04	0.01
Cedar LNG tug in-product	0.02	0.023	0.016	0.00	0.06		0.02	0.017	0.012	0.00	0.04	0.00
Fishing < 60m	0.02	0.023	0.015	0.00	0.05		0.01	0.015	0.012	0.00	0.04	0.01
LNG Canada tanker in-heel	0.09	0.087	0.026	0.04	0.14		0.07	0.071	0.025	0.03	0.12	0.02
LNG Canada tanker in-product	0.08	0.085	0.027	0.04	0.14		0.07	0.071	0.025	0.02	0.12	0.01
LNG Canada tug in-heel	0.02	0.024	0.014	0.00	0.06		0.02	0.016	0.012	0.00	0.04	0.00
LNG Canada tug in-product	0.02	0.022	0.015	0.00	0.05		0.01	0.015	0.012	0.00	0.04	0.01
Other < 40m	0.02	0.021	0.014	0.00	0.05		0.01	0.016	0.013	0.00	0.05	0.01
Other > 100m	0.04	0.046	0.021	0.01	0.09		0.04	0.038	0.021	0.01	0.09	0.00
Other > 40m	0.03	0.033	0.017	0.01	0.07		0.03	0.026	0.016	0.00	0.06	0.00
Passenger > 180m	0.06	0.061	0.023	0.02	0.11		0.05	0.051	0.020	0.01	0.10	0.01
Pleasurecraft < 40m	0.02	0.019	0.014	0.00	0.05		0.01	0.015	0.012	0.00	0.04	0.01
Sailing	0.02	0.019	0.013	0.00	0.05		0.01	0.014	0.012	0.00	0.04	0.01
Towing < 50m	0.02	0.024	0.015	0.00	0.05		0.02	0.020	0.014	0.00	0.05	0.00
Tug < 50m	0.02	0.024	0.015	0.00	0.06		0.02	0.019	0.014	0.00	0.05	0.00

 

Figure S16. Distributions of close-encounter rate estimates for each vessel type (row) and each whale species (color), based upon iterative simulations. Vertical lines indicate the median of each distribution. Here summertime and wintertime distributions are pooled.

 

Depth distribution

Table S15. Proportion of time fin whale spend above various depth cutoffs (1m, 2m, …, 30m), estimated for day and night separately based upon the mean and SD from six SPLASH-10 tag deployments.

	Daytime		Nighttime
Depth (m)	Mean	SD	Mean	SD
1	8.7%	4.8%	8.3%	7.5%
2	14.4%	7.2%	18.1%	13.1%
5	26%	5.2%	41.1%	16.1%
10	37.1%	7.1%	59.2%	15.3%
15	47.5%	8%	72.1%	17%
20	55.4%	6.9%	82%	15.6%
25	60.9%	6.6%	85.3%	16%
30	63.3%	6.4%	89.5%	12.9%

 

Figure S17. Daytime (pink) and nighttime (teal) depth distribution curves for fin whale in and near the Kitimat Fjord System, representing the average proportion of time spent above a given depth across all tag deployments (n=6 in 2013 and 2014). Points on the left side of the plot represent the SD at each depth.

 

Interaction rates

Table S16. Predictions of whale-vessel interaction rates for all vessel types in each traffic scheme.

		Fin whales				Humpback whales
Traffic scheme	Event	Mean	Median	95% CI	80% Conf.	Mean	Median	95% CI	80% Conf.
AIS 2019	Cooccurrence	509.21	509.0	471 - 549	488	5958.69	5961.0	5820 - 6099	5887.0
	Close encounter	13.59	13.5	8 - 19	10	119.59	120.0	102 - 138	110.8
	Strike-zone event	3.06	3.0	1 - 6	2	25.56	25.0	17 - 34	21.0
	(1.5x draft)	3.00	3.0	0 - 6	2	25.59	25.0	18 - 34	21.0
AIS 2030	Cooccurrence	855.99	856.0	802 - 912	827	9428.00	9428.0	9222 - 9638	9322.8
	Close encounter	22.79	22.5	16 - 31	19	184.71	184.0	164 - 209	173.0
	Strike-zone event	4.89	5.0	2 - 9	3	39.13	39.0	29 - 50	34.0
	(1.5x draft)	5.03	5.0	2 - 9	3	38.87	38.5	30 - 49	34.0
LNG Canada	Cooccurrence	137.66	137.0	116 - 161	127	1710.28	1710.0	1637 - 1786	1670.0
	Close encounter	7.20	7.0	3 - 12	5	70.41	70.0	57 - 85	63.0
	Strike-zone event	3.01	3.0	0 - 6	1	30.11	30.0	22 - 39	25.0
	(1.5x draft)	3.01	3.0	0 - 6	1	30.13	30.0	21 - 40	25.0
Cedar LNG	Cooccurrence	19.40	19.0	13 - 27	16	236.57	237.0	211 - 262	223.0
	Close encounter	1.06	1.0	0 - 3	0	9.75	10.0	5 - 15	7.0
	Strike-zone event	0.44	0.0	0 - 2	0	4.37	4.0	1 - 8	3.0
	(1.5x draft)	0.45	0.0	0 - 2	0	4.33	4.0	1 - 8	2.0
Total 2030	Cooccurrence	1013.05	1013.5	954 - 1074	981	11374.85	11373.0	11154 - 11590	11262.8
	Close encounter	31.05	31.0	23 - 40	26	264.87	265.0	239 - 292	251.0
	Strike-zone event	8.34	8.0	4 - 13	6	73.61	73.0	60 - 88	66.0
	(1.5x draft)	8.50	8.0	4 - 14	6	73.33	73.0	59 - 87	66.0

 

 

Figure S18. Distribution of whale-vessel interaction rate predictions for AIS traffic in 2019.

 

Figure S19. Distribution of whale-vessel interaction rate predictions for AIS traffic in 2030.

 

Figure S20. Distribution of whale-vessel interaction rate predictions for LNG Canada traffic in 2030.

 

Figure S21. Distribution of whale-vessel interaction rate predictions for Cedar LNG traffic in 2030.

 

Figure S22. Distribution of whale-vessel interaction rate predictions for all traffic in 2030 (AIS and LNG combined).

 

 

Shares of interaction risk by vessel

 

Table S17. Share of interactions risk attributable to each vessel type, in 2019 and in 2030.

		Fin whales			Humpback whales
Year	Vessel	Cooccurrence	Close encounter	Strike-zone event	Cooccurrence	Close encounter	Strike-zone event
2019	Cargo > 180m	2	5	11	2	5	12
	Fishing < 60m	13	11	6	16	12	7
	Other < 40m	3	3	1	8	7	4
	Other > 100m	3	5	7	7	13	20
	Other > 40m	11	12	11	10	12	12
	Passenger > 180m	7	17	27	3	8	13
	Pleasurecraft < 40m	27	18	7	23	18	8
	Sailing	11	8	3	10	6	3
	Towing < 50m	14	12	16	9	9	11
	Tug < 50m	10	8	11	11	10	11
2030	Cargo > 180m	2	4	6	2	4	7
	Cedar LNG tanker in-heel	0	1	2	1	1	3
	Cedar LNG tanker in-product	0	1	2	1	2	3
	Cedar LNG tug in-heel	0	0	0	1	0	0
	Cedar LNG tug in-product	0	0	0	1	0	0
	Fishing < 60m	12	9	4	16	10	4
	LNG Canada tanker in-heel	3	9	17	4	11	19
	LNG Canada tanker in-product	3	9	15	4	11	18
	LNG Canada tug in-heel	3	3	2	4	3	2
	LNG Canada tug in-product	3	2	2	4	2	2
	Other < 40m	0	0	0	0	0	0
	Other > 100m	2	3	3	5	8	9
	Other > 40m	7	7	6	7	7	6
	Passenger > 180m	7	13	18	3	7	9
	Pleasurecraft < 40m	24	15	4	22	15	5
	Sailing	13	9	3	13	7	2
	Towing < 50m	11	9	9	8	6	6
	Tug < 50m	7	5	6	8	7	5

 

Figure S23. Share of interactions risk attributable to each vessel type, in 2019 and in 2030, for fin whales.

 

Figure S24. Share of interaction risk attributable to each vessel type, in 2019 and in 2030, for humpback whales.

 

Shares of interaction risk by waterway

 

Table S18. Share of interactions risk attributable to each waterway, in 2019 and in 2030.

		Fin whales			Humpback whales
Year	Channel	Cooccurrence	Close encounter	Strike-zone event	Cooccurrence	Close encounter	Strike-zone event
2019	Caamano	61	60	63	14	14	17
	Estevan	0	0	0	5	5	5
	Campania	15	16	17	4	5	6
	Squally	24	23	20	8	7	6
	Whale	0	0	0	39	38	37
	Wright	0	0	0	12	12	14
	McKay	0	0	0	16	15	13
	Verney	0	0	0	4	3	2
2030	Caamano	51	44	37	12	11	10
	Estevan	0	0	0	6	7	8
	Campania	13	12	10	4	4	3
	Squally	35	43	52	11	13	16
	Whale	1	1	1	43	46	49
	Wright	0	0	0	9	8	7
	McKay	0	0	0	12	10	6
	Verney	0	0	0	3	2	1

 

Figure S25. Share of interaction risk attributable to each waterway, in 2019 and in 2030, for fin whales.

 

Figure S26. Share of interactions risk attributable to each waterway, in 2019 and in 2030, for humpback whales.

 

Shares of interaction risk by month

 

Table S19. Share of interactions risk attributable to each month, in 2019 and in 2030.

		Fin whales			Humpback whales
Year	Month	Cooccurrence	Close encounter	Strike-zone event	Cooccurrence	Close encounter	Strike-zone event
2019	Jan	0	0	0	0	0	0
	Feb	0	0	0	0	0	0
	Mar	0	0	0	0	0	0
	Apr	0	0	0	0	0	0
	May	5	6	7	3	3	4
	Jun	14	15	15	16	16	17
	Jul	21	21	19	10	10	9
	Aug	37	34	31	34	33	29
	Sep	16	17	19	32	32	33
	Oct	5	6	6	4	5	6
	Nov	1	1	1	1	1	1
	Dec	0	0	0	0	0	0
2030	Jan	0	0	0	0	0	0
	Feb	0	0	0	0	0	0
	Mar	0	0	0	0	0	0
	Apr	0	1	1	0	0	0
	May	5	5	7	3	3	3
	Jun	14	15	15	16	16	16
	Jul	21	21	22	11	11	11
	Aug	37	33	27	37	36	35
	Sep	16	17	20	28	28	28
	Oct	6	6	7	4	5	6
	Nov	1	1	1	1	1	1
	Dec	0	0	0	0	0	0

 

Figure S27. Share of interactions risk attributable to each month, in 2019 and in 2030, for fin whales.

 

Figure S28. Share of interactions risk attributable to each month, in 2019 and in 2030, for humpback whales.

 

Shares of interaction risk by diel period

 

Table S20. Share of interaction risk attributable to each diel period, in 2019 and in 2030.

		Fin whales			Humpback whales
Year	Diel period	Cooccurrence	Close encounter	Strike-zone event	Cooccurrence	Close encounter	Strike-zone event
2019	day	89	87	78	83	79	65
	night	11	13	22	17	21	35
2030	day	87	84	71	82	78	64
	night	13	16	29	18	22	36

 

Figure S29. Share of interactions risk attributable to each diel period, in 2019 and in 2030, for fin whales.

 

Figure S30. Share of interactions risk attributable to each diel period, in 2019 and in 2030, for humpback whales.

 

Collisions & mortalities

 

Table S21. Predicted rates of collision and mortality for each vessel class (> 180m) and each whale species.

			Fin whales				Humpback whales
Traffic scheme	Event	Avoidance	Mean	Median	95% CI	80% Conf.	Mean	Median	95% CI	80% Conf.
AIS 2019	Collision	0.55	0.48	0	0 - 2	0	2.92	3	0 - 6	1
		~ Speed	0.77	1	0 - 2	0	3.89	4	1 - 8	2
		None	1.13	1	0 - 3	0	6.44	6	2 - 11	4
	Mortality	0.55	0.47	0	0 - 2	0	2.73	3	0 - 6	1
		~ Speed	0.74	1	0 - 2	0	3.70	4	1 - 7	2
		None	1.08	1	0 - 3	0	6.06	6	2 - 10	4
AIS 2030	Collision	0.55	0.86	1	0 - 3	0	5.11	5	2 - 9	3
		~ Speed	1.33	1	0 - 3	0	6.93	7	3 - 12	5
		None	1.98	2	0 - 4	1	11.54	11	6 - 17	9
	Mortality	0.55	0.82	1	0 - 2	0	4.78	5	2 - 9	3
		~ Speed	1.29	1	0 - 3	0	6.55	6	3 - 11	4
		None	1.90	2	0 - 4	1	10.80	11	6 - 17	8
LNG Canada	Collision	0.55	1.24	1	0 - 3	0	12.39	12	7 - 19	9
		~ Speed	1.18	1	0 - 3	0	11.69	11	6 - 18	9
		None	2.71	3	0 - 6	1	27.46	27	19 - 37	23
	Mortality	0.55	1.06	1	0 - 3	0	10.55	10	5 - 16	8
		~ Speed	1.00	1	0 - 3	0	10.16	10	5 - 16	7
		None	2.29	2	0 - 5	1	23.43	23	16 - 32	19
Cedar LNG	Collision	0.55	0.17	0	0 - 1	0	1.74	2	0 - 4	1
		~ Speed	0.17	0	0 - 1	0	1.56	1	0 - 4	0
		None	0.41	0	0 - 2	0	3.92	4	1 - 8	2
	Mortality	0.55	0.15	0	0 - 1	0	1.45	1	0 - 4	0
		~ Speed	0.15	0	0 - 1	0	1.31	1	0 - 4	0
		None	0.35	0	0 - 2	0	3.26	3	1 - 6	2
Total 2030	Collision	0.55	2.27	2	0 - 5	1	19.24	19	12 - 26	15
		~ Speed	2.68	3	0 - 6	1	20.18	20	13 - 28	16
		None	5.10	5	2 - 9	3	42.94	43	33 - 54	37
	Mortality	0.55	2.03	2	0 - 4	1	16.78	17	10 - 24	13
		~ Speed	2.44	2	0 - 5	1	18.02	18	12 - 25	14
		None	4.54	4	1 - 8	3	37.48	37	28 - 48	32

 

Figure S31. Posterior distributions of collision and mortality estimates for fin whales (a - b) and humpback whales (c-d), for each traffic scheme we analyzed.

 

 

Figure S32. Share of collision and mortality risk attributable to each vessel type, in 2019 and in 2030.

 

Table S22. Share of collision and mortality risk attributable to each waterway, in 2019 and in 2030.

	Fin whales				Humpback whales
	Collision		Mortality		Collision		Mortality
Waterway	2019	2030	2019	2030	2019	2030	2019	2030
Caamano	59	29	61	30	17	9	17	10
Estevan	0	0	0	0	5	8	4	8
Campania	25	11	23	13	7	4	7	4
Squally	15	59	15	56	5	16	5	16
Whale	0	1	1	1	39	50	39	50
Wright	0	0	0	0	14	7	15	7
McKay	0	0	0	0	12	5	12	5
Verney	0	0	0	0	1	0	1	0

 

Figure S33. Share of collision and mortality risk attributable to each waterway, in 2019 and in 2030.

 

Table S23. Share of collision and mortality risk attributable to each month, in 2019 and in 2030.

	Fin whales				Humpback whales
	Collision		Mortality		Collision		Mortality
Month	2019	2030	2019	2030	2019	2030	2019	2030
1	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0
4	0	1	1	1	0	1	0	1
5	4	5	5	5	3	3	3	3
6	21	18	23	18	17	16	17	17
7	19	21	16	19	11	11	11	11
8	30	27	27	29	30	36	30	35
9	21	19	23	21	32	27	32	27
10	3	7	3	7	5	5	5	5
11	0	1	1	1	1	1	1	1
12	0	0	0	0	0	0	0	0

 

Figure S34. Share of collision and mortality risk attributable to each month, in 2019 and in 2030.

 

Table S24. Share of collision and mortality risk attributable to each diel period, in 2019 and in 2030.

	Fin whales				Humpback whales
	Collision		Mortality		Collision		Mortality
Diel period	2019	2030	2019	2030	2019	2030	2019	2030
day	80	73	83	72	61	61	60	62
night	20	27	17	28	39	39	40	38

 

Figure S35. Share of collision and mortality risk attributable to each diel period, in 2019 and in 2030.

 

Chances of certain outcome severities

 

Table S25. Chances of various impact severities for fin whales and humpback whales, due to present-day AIS-transmitting traffic (represented by 2019 traffic), projected AIS-transmitting traffic in 2030, projected LNG Canada traffic, projected Cedar LNG traffic, then all traffic in 2030 (previous categories combined).

		AIS 2019		AIS 2030		LNG Canada		Cedar LNG		All traffic 2030
Species	Chances (%) of…	Coll.	Mort.	Coll.	Mort.	Coll.	Mort.	Coll.	Mort.	Coll.	Mort.
Fin whale	Zero	33.1	37.7	14.0	19.1	28.5	33.7	83.4	85.6	3.8	6.0
	At least 1	66.9	62.3	86.0	80.9	71.5	66.3	16.6	14.4	96.2	94.0
	At least 2	32.9	26.6	55.0	45.8	34.8	27.1	1.3	0.9	83.7	76.0
	At least 3	11.1	7.5	27.1	20.9	13.8	9.3	0.0	0.0	65.2	52.8
	At least 4	3.4	1.6	11.7	8.5	4.8	2.1	0.0	0.0	41.9	30.9
	At least 5	0.6	0.2	5.2	2.6	0.9	0.4	0.0	0.0	24.2	16.5
Humpback whale	Zero	0.0	0.3	0.0	0.0	0.0	0.0	18.7	25.4	0.0	0.0
	At least 1	100.0	99.7	100.0	100.0	100.0	100.0	81.3	74.6	100.0	100.0
	At least 2	99.8	99.4	100.0	100.0	100.0	100.0	49.4	38.3	100.0	100.0
	At least 3	99.1	97.9	100.0	100.0	100.0	99.8	24.3	18.1	100.0	100.0
	At least 4	97.8	96.1	100.0	99.8	99.9	99.6	9.7	6.2	100.0	100.0
	At least 5	95.6	89.8	99.9	99.4	99.5	98.6	3.0	1.3	100.0	100.0

 

Table S26. Chances of various impact severities for fin whales and humpback whales, similar to above, now described as the chances of experiencing no more than the stated number of events.

		AIS 2019		AIS 2030		Cedar LNG		LNG Canada		All traffic 2030
Species	Chances (%) of…	Coll.	Mort.	Coll.	Mort.	Coll.	Mort.	Coll.	Mort.	Coll.	Mort.
Fin whale	Zero	33.1	37.7	14.0	19.1	83.4	85.6	28.5	33.7	3.8	6.0
	Max of 1	67.1	73.4	45.0	54.2	98.7	99.1	65.2	72.9	16.3	24.0
	Max of 2	88.9	92.5	72.9	79.1	100.0	100.0	86.2	90.7	34.8	47.2
	Max of 3	96.6	98.4	88.3	91.5	100.0	100.0	95.2	97.9	58.1	69.1
	Max of 4	99.4	99.8	94.8	97.4	100.0	100.0	99.1	99.6	75.8	83.5
	Max of 5	99.7	99.8	98.3	99.3	100.0	100.0	99.6	99.8	87.6	93.1
Humpback whale	Zero	0.0	0.3	0.0	0.0	18.7	25.4	0.0	0.0	0.0	0.0
	Max of 1	0.2	0.6	0.0	0.0	50.6	61.7	0.0	0.0	0.0	0.0
	Max of 2	0.9	2.1	0.0	0.0	75.7	81.9	0.0	0.2	0.0	0.0
	Max of 3	2.2	3.9	0.0	0.2	90.3	93.8	0.1	0.4	0.0	0.0
	Max of 4	4.4	10.2	0.1	0.6	97.0	98.7	0.5	1.4	0.0	0.0
	Max of 5	9.9	20.7	0.5	1.7	99.2	99.8	1.2	4.3	0.0	0.0

 

---

Validation

 

Fin whales

## Melting outcomes & prepping the posterior ...
## Determining the probability of your observations ...
## Likelihood of your observation, assuming perfect detection = 0.003
## Finding the strike detection rate (SDR) that would make your observations plausible ...
## preparing L ~ SDR plot ...
## --- SDR needed for P(Observation) of 0.05 = 0.455
## --- SDR needed for P(Observation) of 0.10 = 0.355
## --- SDR needed for P(Observation) of 0.20 = 0.235
## --- SDR needed for P(Observation) of 0.55 = 0.095

Figure S36. Results of ship-strike model validation for fin whales, in which the likelihood of not observing a strike in the last decade was estimated according to our model results. Left: Distribution of strike observations predicted with our models when assuming perfect detection (i.e., no strikes missed). The red dashed line indicates what we actually observed. Right: The probability of our observations under various scenarios of imperfect detection. Dashed lines indicate conventional alpha levels of significance.

 

Humpback whales

## Melting outcomes & prepping the posterior ...
## Determining the probability of your observations ...
## Likelihood of your observation, assuming perfect detection = 0
## Finding the strike detection rate (SDR) that would make your observations plausible ...
## preparing L ~ SDR plot ...
## --- SDR needed for P(Observation) of 0.05 = 0.16
## --- SDR needed for P(Observation) of 0.10 = 0.13
## --- SDR needed for P(Observation) of 0.20 = 0.085
## --- SDR needed for P(Observation) of 0.55 = 0.03

Fig S37. Results of ship-strike model validation for humpback whales, in which the likelihood of not observing a strike in the last decade was estimated according to our model results. Left: Distribution of strike observations predicted with our models when assuming perfect detection (i.e., no strikes missed). The red dashed line indicates what we actually observed. Right: The probability of our observations under various scenarios of imperfect detection. Dashed lines indicate conventional alpha levels of significance.

---

Mitigation measures

Scenarios 3 (rescheduling) & 4 (moratoria)

Figure S38. Efficacy of mitigation categories 3 (LNG rescheduling) and 4 (LNG moratoria) for fin whales, when those measures are applied to different months of the year for various durations (one - three months).

Figure S39. Efficacy of mitigation categories 3 (LNG rescheduling) and 4 (LNG moratoria) for humpback whales, when those measures are applied to different months of the year for various durations (one - three months).

---

Discussion

Density estimate comparison throughout region

  # Fin whales (Gitga'at average)
  0.014 / 0.007 # eez (Wright)
## [1] 2
  0.014 / 0.002 # north coast (Wright)
## [1] 7
  0.014 / 0.003 # vancouver island (Nichol)
## [1] 4.666667

  # Fin whales (Squally Ch)
  0.031 / 0.007 # eez
## [1] 4.428571
  0.031 / 0.002 # north coast
## [1] 15.5
  0.031 / 0.003
## [1] 10.33333

  # Humpback whales (Gitga'at average)
  0.079 / 0.016 # eez (Wright)
## [1] 4.9375
  0.079 / 0.025 # north coast (Wright)
## [1] 3.16
  0.079 / 0.014 # vancouver island (Nichol)
## [1] 5.642857

  # Humpback whales (Wright Sound)
  0.0117 / 0.016 # eez
## [1] 0.73125
  0.0117 / 0.025 # north coast
## [1] 0.468
  0.0117 / 0.014
## [1] 0.8357143

---